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Atomic partial charges are local, model-dependent descriptors that often fail to capture the global electrostatic environment governing chemical reactivity. This study demonstrates that the molecular …

Atomic partial charges are local, model-dependent descriptors that often fail to capture the global electrostatic environment governing chemical reactivity. This study demonstrates that the molecular electrostatic potential (ESP) at the Si–H hydrogen in trisubstituted silanes reliably predicts electrophilic versus nucleophilic behavior, whereas local charges alone can mislead. Electron-donating substituents generate hydridic hydrogens with negative ESP near hydrogen, favoring nucleophilicity. Electron-withdrawing substituents typically leave hydrogen with a negative local charge but generate a positive ESP region along the Si–H axis, promoting electrophilic character. Increasing solvent polarity amplifies these contrasts, driving minima more negative for electron-donating substituents and maxima more positive for electron-withdrawing substituents. The resulting positive region near hydrogen in electron-withdrawing silanes is directionally σ-hole-like, yet unlike classical σ-holes from lone-pair depletion, it reflects a collective molecular electrostatic effect. These findings highlight the importance of global electrostatics for understanding structure–reactivity relationships and predicting substituent-controlled behavior in silicon hydrides.

This dataset supports the study “Bright and Consolidated Wind Streaks at Oxia Planum: Implications for the ExoMars 2028 Mission” and contains two components: Data Set S1: A GIS shapefile o…

This dataset supports the study “Bright and Consolidated Wind Streaks at Oxia Planum: Implications for the ExoMars 2028 Mission” and contains two components: Data Set S1: A GIS shapefile of 902 aeolian linear features mapped at Oxia Planum, Mars, comprising 808 bright wind streaks, 92 dark stripes, and two crater ejecta streaks. Attributes include feature classifications and azimuths measured clockwise from geographic north. The accompanying README describes the attribute fields, coordinate system, and mapping conventions. Data Set S2: The HiRISE–CaSSIS pansharpened image shown in Figure 8b, generated from CaSSIS observation MY35_013584_163_0 and HiRISE observation ESP_046512_1980 using the Adaptive Gram–Schmidt method implemented in PANCO.

This study examined the levels of oxidative stress markers in type 2 diabetic patients within Owerri, Imo State, Nigeria. A comparative cross-sectional study was adopted. The study population consiste…

This study examined the levels of oxidative stress markers in type 2 diabetic patients within Owerri, Imo State, Nigeria. A comparative cross-sectional study was adopted. The study population consisted of one hundred (100) diagnosed type 2 diabetic patients and one hundred (100) apparently healthy, age- and sex-matched non-diabetic individuals who served as controls. All participants were aged between 30 and 70 years. Ten (10) mL of venous blood samples were collected from each participant after overnight fast using standard aseptic procedures and distributed into appropriate tubes for the determination of fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), insulin, lipid profile, and oxidative stress markers. Serum and plasma were prepared by centrifugation at 3000 rpm for 10 minutes, while whole blood was used for selected analyses. The samples were analyzed using standard laboratory methods. Data obtained were analyzed using Statistical Package for Social Sciences (SPSS) version 25.0. The result of the findings revealed that oxidative stress markers SOD, CAT, and GPx were significantly lower (p=0.000 for all) while MDA was significantly higher (p=0.000) in the diabetic group. The study concluded that the levels of oxidative stress markers among patients with type 2 diabetes mellitus and apparently healthy controls in Owerri, Imo State, Nigeria. The findings demonstrated significant differences in the levels of the antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), as well as the lipid peroxidation marker malondialdehyde (MDA), between the two groups. One of the recommendations made was that healthcare professionals should consider oxidative stress markers such as SOD, CAT, GPx, and MDA as complementary biochemical parameters in the assessment and monitoring of patients with type 2 diabetes mellitus, where such testing is available.

This study examined the intersection of crime, conflict, and community security in Bangladesh’s Chittagong Hill Tracts (CHT). The signing of the Chittagong Hill Tracts Peace Accord in 1997 did n…

This study examined the intersection of crime, conflict, and community security in Bangladesh’s Chittagong Hill Tracts (CHT). The signing of the Chittagong Hill Tracts Peace Accord in 1997 did not solve the insecurity problems caused by crime, land disputes, ethnic tensions, political rivalry, and poor confidence in security institutions in the region. The study focuses on Rangamati, Khagrachhari and Bandarban districts and evaluates the effect of these elements on the safety perception of the people. This study used a quantitative cross-sectional design with primary data collected from 400 respondents using a structured questionnaire. Sample size was calculated by Cochran’s formula and participants were picked by multistage sampling procedure. The questionnaire covered criminal victimization, fear of crime, exposure to war, land insecurity, security institutions’ trust and perceived community security. Data were examined with descriptive statistics, Pearson correlation, multiple regression, independent-sample t-tests and one-way ANOVA in SPSS. The study hypothesizes that criminal victimization, conflict exposure and land insecurity diminish perceived community security but trust in police, local administration and community-based institutions increase perceived community security. The study offers quantitative evidence to the literature on peacebuilding, public safety, local governance, and conflict prevention in the CHT by comparing the three hill districts. The findings are meant to help policy makers and local stakeholders in building more inclusive and context sensitive methods to enhance community security.

A resource to support recently onboarded member organisations to The Carpentries. 

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Description This repository contains the research data for "CEFFD: A benchmark dataset and evaluation framework for freshwater fish detection in Central European fishways" [under review]. This in…

Description This repository contains the research data for "CEFFD: A benchmark dataset and evaluation framework for freshwater fish detection in Central European fishways" [under review]. This includes the proposed benchmark's dataset (dataset.zip) and evaluation framework (benchmark.zip) for fish detection training and evaluation in Central European fishways. This benchmark can be freely used and extended to support transparent and reliable automated fish detection in fishways. Additionally, all model weights, training scripts, raw and formatted results are provided to support the reproducibility of the study. The dataset contains 85,931 annotated frames and 94,462 fish bounding boxes (no species labels) collected from five Central European rivers: the Main, Moselle, Elde, Aller and Neckar. The training subset contains 58,897 frames dedicated to a five-fold training loop (roughly 47,117 training and 11,780 validation frames per cross-validation). The evaluation subset contains 27,034 frames. The Aller and Neckar rivers are held out for cross-site evaluation (5,942 frames for Aller River and 5,959 frames for Neckar River). Frames originate from 12 video collections, which are used in the benchmark to determine evaluation subsets: 13 14 51 52 29 30 43 44 100 101 2425 1213 Main Main Main Main Elde Elde Elde Elde Moselle Moselle Neckar Aller Therefore, subset 13-101 corresponds to the Main, Moselle and Elde rivers, 1213 corresponds to the Aller River, and 2425 corresponds to the Neckar River. The benchmark has three tasks: fish detection, fish/no-fish video classification and fish instance counting (more details can be seen in README.md in benchmark.zip). Below are fish detection results on all evaluation frames for models used in the study (provided in model_weights.zip): YOLOv5s, YOLOv8s, YOLO11s, YOLO12s and YOLO26s. Model P@thresh R@thresh F1@thresh Threshold P-AUC R-AUC F1-AUC mAP@50 mAP@50-95 YOLOv5s 0.946 0.895 0.920 0.320 0.931 0.777 0.824 0.942 0.728 YOLOv8s 0.950 0.889 0.919 0.322 0.923 0.751 0.799 0.937 0.734 YOLO11s 0.950 0.881 0.914 0.324 0.922 0.741 0.793 0.928 0.726 YOLO12s 0.951 0.886 0.918 0.289 0.925 0.736 0.789 0.933 0.732 YOLO26s 0.938 0.850 0.892 0.219 0.921 0.696 0.765 0.906 0.706 Content dataset_sample.zip - Contains a small sample (preview) of the dataset to check the content and the structure before downloading the dataset.zip. dataset.zip - Full dataset containing dataset.json meta-file and images/ folder with frames. benchmark.zip - Contains evaluation scripts (evaluate.py and analyse_results.py) for model evaluation. The benchmark is executed from the command line. An example notebook is provided (also used in the study) to automate the evaluation of all models using the benchmark. model_weights.zip - Contains 25 model files (five per architecture) as well as the training scripts these models were trained with. raw_results.zip - Contains the results of running the evaluate.py over all models (contains also extra results, such as confidence-curve axis values). formatted_results.zip - Results presented within the study. Obtained by running analyse_results.py over raw results. How to use To use the benchmark, download both dataset.zip and benchmark.zip. Custom datasets can be made, following the same dataset structure. Similarly, the benchmark can be extended for other models (not restricted to the YOLO family) following the README.md. To get the results, either download formatted_results.zip or download benchmark.zip and raw_results.zip and run the analyse_results.py script. To reproduce the results,download both dataset.zip and benchmark.zip. Use the convert_dataset.py scripts to convert it into a training format (supported YOLO and COCO). In this study, the YOLO format was used for training. Use the provided training script to train the models. The conversion script will automatically create five cross-validation subsets; therefore, there will be 25 trained models. After training the models, use the last models’ weights with a corresponding model wrapper in benchmark (yolov5 for YOLOv5 models and ultralytics for YOLOv8, YOLO11, YOLO12 and YOLO26). Run the benchmark, which will produce 25 result folders, one for each model. Finally, use the benchmark’s analysis script to format results and average them with each model’s architecture. Acknowledgements This research was funded in part by the Estonian Research Council grant No. PRG2198 "MultiFlow – Multiscale Natural Flow Sensing for Coasts and Rivers” and the European Union via grant No. TEM-TA141. The authors express their gratitude to Maris Koppel, Jevgeni Pakin, Gert Toming and Helena Carmen Udu for their help and effort during data annotation. The data used in this study was selected from a larger dataset of over 380,000 videos, collected and manually annotated in the project "Fishway efficiency evaluation", funded by the German Ministry of Transport.